Last updated: 2019-08-19
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Knit directory: ~/Research-Local/RNAseq-Local/TCGA-Nigerian-RNAseq/
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#Translation from HTSeq raw counts -> Count Matrix I have 68 TCGA patients with whole-genome sequencing data and RNAseq data as well as 50 Nigerian patients with RNA-seq data. Raw counts were initially processed using HTSeq, so HTSeq data is being formatted for use with DESeq2 and limma-voom.
FOLDER <- "/Users/parajago/Research-Local/RNAseq-Local/Inputs/NigerianTCGA_quants-proteincoding-IHC"
sampleFiles <- grep("htseq.counts",list.files(FOLDER),value=TRUE)
#Differential gene expression setup based on race (b/w/other)
sampleConditionrace <- sampleFiles
countVar2=1
for (sample in sampleConditionrace){
if (stri_detect_fixed(sample,"LIB")==TRUE){
sampleConditionrace[countVar2] <- "Nigerian"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"black")==TRUE){
sampleConditionrace[countVar2] <- "TCGA_black"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"white")==TRUE){
sampleConditionrace[countVar2] <- "TCGA_white"
countVar2=countVar2+1
} else{
sampleConditionrace[countVar2] <- "TCGA_other"
countVar2=countVar2+1
}
}
#Condition based on IHC subtype
sampleConditionIHC <- sampleFiles
countVar2=1
for (sample in sampleConditionIHC){
if (stri_detect_fixed(sample,"Her2")==TRUE){
sampleConditionIHC[countVar2] <- "Her2"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"TNBC")==TRUE){
sampleConditionIHC[countVar2] <- "TNBC"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"HRpos")==TRUE){
sampleConditionIHC[countVar2] <- "HRpos"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"TPBC")==TRUE){
sampleConditionIHC[countVar2] <- "TPBC"
countVar2=countVar2+1
} else{
sampleConditionIHC[countVar2] <- "Other"
countVar2=countVar2+1
}
}
#Condition based on batch (relevant to the Nigerian patients only; no difference in batch for the TCGA patients)
batchval <- sampleFiles
countVar2=1
for (sample in batchval){
if (stri_detect_fixed(sample,"batch1")==TRUE){
batchval[countVar2] <- "batch1"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"batch23")==TRUE){
batchval[countVar2] <- "batch23"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"batch4")==TRUE){
batchval[countVar2] <- "batch4"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"batch5")==TRUE){
batchval[countVar2] <- "batch5"
countVar2=countVar2+1
} else{
batchval[countVar2] <- "batchT"
countVar2=countVar2+1
}
}
sampleLOH <-sampleFiles
countVar2=1
for (sample in sampleLOH){
if (stri_detect_fixed(sample,"noLOH")==TRUE){
sampleLOH[countVar2] <- "noLOH"
countVar2=countVar2+1
} else if (stri_detect_fixed(sample,"LOH")==TRUE){
sampleLOH[countVar2] <- "LOH"
countVar2=countVar2+1
} else{
sampleLOH[countVar2] <- NA
countVar2=countVar2+1
}
}
table(sampleConditionrace, sampleConditionIHC)
sampleConditionIHC
sampleConditionrace Her2 HRpos TNBC
Nigerian 23 7 20
TCGA_black 0 12 20
TCGA_white 10 10 16
sampleTable <- data.frame(sampleName=gsub(".htseq.counts","",sampleFiles),
fileName=sampleFiles,
condition1=sampleConditionrace,
condition2=sampleConditionIHC,
condition3=sampleLOH,
batch=batchval)
sampleTable$sampleCondition <- paste(sampleTable$condition1, sampleTable$condition2, sep=".")
ddsHTSeqMF <- DESeqDataSetFromHTSeqCount(sampleTable=sampleTable,
directory=FOLDER,
design=~sampleCondition)
ddsHTSeqMF <- ddsHTSeqMF[rowSums(counts(ddsHTSeqMF)) > 0, ] #Pre-filtering the dataset by removing the rows without any information about gene expression -> this removes 675 genes
#Quantile normalization Please refer to: https://parajago.github.io/TCGA-Nigerian-RNAseq/NigerianTCGArawcountsDeSeq2-pc2.html regarding comparison between the Nigerian and TCGA data sets and why quantile normalization under the limma-voom approach was chosen for primary differential expression analysis.
##Data visualization
countmatrix <- assay(ddsHTSeqMF) #Raw counts organized into matrix format from individual files
countmatrix2 <- log2(countmatrix + 1) #Basic transformation of the count data
plot(density(countmatrix2[,1]),lwd=3,ylim=c(0,.30), main="Density of counts with log2[count]+1 transformation ONLY")
for(i in 1:118){lines(density(countmatrix2[,i]),lwd=3)} #This demonstrates that there is a difference in distributions between the Nigerian and TCGA data with basic log transformation normalization
norm_countmatrix <- as.matrix(countmatrix2)
norm_countmatrix = normalize.quantiles(norm_countmatrix)
plot(density(norm_countmatrix[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization")
for(i in 1:118){lines(density(norm_countmatrix[,i]),lwd=3)} #This demonstrates the effect of comparative quantile normalization
colnames (norm_countmatrix) <- colnames (countmatrix2)
rownames (norm_countmatrix) <- rownames (countmatrix2)
norm_countmatrix <- as.data.frame(norm_countmatrix)
countmatrixNigerian <- dplyr::select(norm_countmatrix, contains("LIB"))
plot(density(countmatrixNigerian[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization - Nigerian")
for(i in 1:50){lines(density(countmatrixNigerian[,i]),lwd=3)} #This demonstrates the result of the normalized Nigerian counts separately
tcgacolnames <- colnames(countmatrix)
tcgacolnames <- setdiff(tcgacolnames, colnames(countmatrixNigerian))
countmatrixTCGA <- norm_countmatrix[ , tcgacolnames]
plot(density(countmatrixTCGA[,1]),lwd=3,ylim=c(0,.3), main="Density of counts with quantile normalization - TCGA")
for(i in 1:68){lines(density(countmatrixTCGA[,i]),lwd=3)} #This demonstrates the result of the normalized TCGA counts separately
norm_countmatrix <- as.data.frame(norm_countmatrix)
t_norm_countmatrix <- t(norm_countmatrix)
t_norm_countmatrix <- cbind (t_norm_countmatrix, sampleTable) #This binds the characteristics of the original patients to the quantile normalized counts. CBinding was checked to make sure that patients were correctly aligned to characteristics.
quant.pca <- prcomp(t_norm_countmatrix[,1:19652])
autoplot(quant.pca, data=t_norm_countmatrix, colour='sampleCondition', main="PCA of quantile normalization results prior to DE analysis")
In the raw data with log transformation only, we are able to see that there are two peaks corresponding to the two datasets (Nigerian and TCGA). The quantile normalization demonstrates a PCA that has similar clustering. Only ~20% of the distribution of the data set is explained by the PCA1, 2 of the variables.
##Differential expression setup
annotation <- as.data.frame(row.names(countmatrix))
colnames(annotation) <- c("GeneID")
annotation$temp <- gsub("[.].+", "", annotation[,1])
annotation$symbol <- mapIds(EnsDb.Hsapiens.v75,
keys=annotation$temp,
column="SYMBOL",
keytype="GENEID",
multiVals="first")
annotation$symbol <- mapIds(EnsDb.Hsapiens.v75,
keys=annotation$temp,
column="SYMBOL",
keytype="GENEID",
multiVals="first")
annotation$chr <- mapIds(EnsDb.Hsapiens.v75,
keys=annotation$temp,
column="SEQNAME",
keytype="GENEID",
multiVals="first")
annotation$locstart <- mapIds(EnsDb.Hsapiens.v75,
keys=annotation$temp,
column="GENESEQSTART",
keytype="GENEID",
multiVals="first")
annotation$locend <- mapIds(EnsDb.Hsapiens.v75,
keys=annotation$temp,
column="GENESEQEND",
keytype="GENEID",
multiVals="first")
annotation$temp <- NULL
design <- t_norm_countmatrix
design <- design %>% dplyr::select(sampleCondition)
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian vs. TCGA regardless of subtype
designNvsT <- t_norm_countmatrix
designNvsT <- designNvsT %>% dplyr::select(condition1)
designNvsT$condition1 <- ifelse (designNvsT$condition1=="TCGA_white", 0, as.character(designNvsT$condition1))
designNvsT$condition1 <- ifelse (designNvsT$condition1=="TCGA_black", 1, as.character(designNvsT$condition1))
designNvsT$condition1 <- ifelse (designNvsT$condition1=="Nigerian", 2, as.character(designNvsT$condition1))
designNvsT$TCGA_white <- ifelse (designNvsT$condition1==0, 1, 0)
designNvsT$TCGA_black <- ifelse (designNvsT$condition1==1, 1, 0)
designNvsT$Nigerian <- ifelse (designNvsT$condition1==2, 1, 0)
designNvsT$condition1 <- NULL
mm <- model.matrix(~0+designNvsT$TCGA_white+designNvsT$TCGA_black+designNvsT$Nigerian)
quantids <- rownames(designNvsT)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "TCGA_black", "Nigerian")
quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)
d0 <- DGEList(counts=quantdata, genes=annotation)
dim(d0)
[1] 19652 118
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # Number of genes after taking out low expressed genes
[1] 16711 118
v=voom(d,designNvsT,plot=T, normalize="quantile")
contr.matrix <- makeContrasts(TCGA_white-Nigerian,levels=colnames(designNvsT))
fit <- lmFit(v, designNvsT)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit, trend=TRUE)
dt <- decideTests(fit)
summary(dt)
TCGA_white - Nigerian
Down 3440
NotSig 9111
Up 4160
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression between breast cancers in\n Nigerian vs. TCGA white breast cancer patients\n quantile corrected")
qvals<-p.adjust(fit$p.value[,1], method='fdr')
df_limma <- data_frame(log2FoldChange = fit$coefficients[,1],
pval = fit$p.value[,1],
padj = qvals,
anno = fit$genes)
with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian and TCGA white patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))
df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)
top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAwhite-Nigerian-all-DE.csv", row.names = FALSE)
contr.matrix <- makeContrasts(TCGA_black-Nigerian,levels=colnames(designNvsT))
fit <- lmFit(v, designNvsT)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit, trend=TRUE)
dt <- decideTests(fit)
summary(dt)
TCGA_black - Nigerian
Down 3132
NotSig 9587
Up 3992
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression between breast cancers in\n Nigerian vs. TCGA black breast cancer patients\n quantile corrected")
qvals<-p.adjust(fit$p.value[,1], method='fdr')
df_limma <- data_frame(log2FoldChange = fit$coefficients[,1],
pval = fit$p.value[,1],
padj = qvals,
anno = fit$genes)
with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian and TCGA black patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))
df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)
top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAblack-Nigerian-all-DE.csv", row.names = FALSE)
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA White - TNBC
designNvsW <- design
designNvsW$sampleCondition <- ifelse (designNvsW$sampleCondition=="TCGA_white.TNBC", 0, as.character(designNvsW$sampleCondition))
designNvsW$sampleCondition <- ifelse (designNvsW$sampleCondition=="Nigerian.TNBC", 1, as.character(designNvsW$sampleCondition))
designNvsW$sampleCondition <- ifelse (designNvsW$sampleCondition==0 | designNvsW$sampleCondition==1, designNvsW$sampleCondition, NA)
designNvsW <- designNvsW %>% subset(is.na(sampleCondition)==FALSE)
designNvsW$TCGA_white.TNBC <- ifelse (designNvsW$sampleCondition==0, 1, 0)
designNvsW$Nigerian.TNBC <- ifelse (designNvsW$sampleCondition==1, 1, 0)
designNvsW$sampleCondition <- NULL
mm <- model.matrix(~0+designNvsW$TCGA_white.TNBC+designNvsW$Nigerian.TNBC)
quantids <- rownames(designNvsW)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "Nigerian")
quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)
d0 <- DGEList(counts=quantdata, genes=annotation)
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # Number of genes after taking out low expressed genes
[1] 14543 36
v=voom(d,designNvsW,plot=T, normalize="quantile")
contr.matrix <- makeContrasts(TCGA_white.TNBC-Nigerian.TNBC, levels=colnames(designNvsW))
fit <- lmFit(v, designNvsW)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
TCGA_white.TNBC - Nigerian.TNBC
Down 1998
NotSig 9679
Up 2866
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between TNBC breast cancers in Nigerian and TCGA white patients\n quantile corrected")
qvals<-p.adjust(fit$p.value[,1], method='fdr')
df_limma <- data_frame(log2FoldChange = fit$coefficients[,1],
pval = fit$p.value[,1],
padj = qvals,
anno = fit$genes)
with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between TNBC \nbreast cancers in Nigerian and TCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))
df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)
top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAwhite-TNBC.csv", row.names = FALSE)
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA Black - TNBC
designNvsB <- design
designNvsB$sampleCondition <- ifelse (designNvsB$sampleCondition=="TCGA_black.TNBC", 0, as.character(designNvsB$sampleCondition))
designNvsB$sampleCondition <- ifelse (designNvsB$sampleCondition=="Nigerian.TNBC", 1, as.character(designNvsB$sampleCondition))
designNvsB$sampleCondition <- ifelse (designNvsB$sampleCondition==0 | designNvsB$sampleCondition==1, designNvsB$sampleCondition, NA)
designNvsB <- designNvsB %>% subset(is.na(sampleCondition)==FALSE)
designNvsB$TCGA_black.TNBC <- ifelse (designNvsB$sampleCondition==0, 1, 0)
designNvsB$Nigerian.TNBC <- ifelse (designNvsB$sampleCondition==1, 1, 0)
designNvsB$sampleCondition <- NULL
mm <- model.matrix(~0+designNvsB$TCGA_black.TNBC+designNvsB$Nigerian.TNBC)
quantids <- rownames(designNvsB)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_black", "Nigerian")
quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)
d0 <- DGEList(counts=quantdata, genes=annotation)
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # Number of genes after taking out low expressed genes
[1] 14642 40
v=voom(d,designNvsB,plot=T, normalize="quantile")
contr.matrix <- makeContrasts(TCGA_black.TNBC-Nigerian.TNBC, levels=colnames(designNvsB))
fit <- lmFit(v, designNvsB)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
TCGA_black.TNBC - Nigerian.TNBC
Down 2125
NotSig 9880
Up 2637
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between TNBC breast cancers in Nigerian and TCGA black patients\n quantile corrected")
qvals<-p.adjust(fit$p.value[,1], method='fdr')
df_limma <- data_frame(log2FoldChange = fit$coefficients[,1],
pval = fit$p.value[,1],
padj = qvals,
anno = fit$genes)
with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between TNBC \nbreast cancers in Nigerian and TCGA black breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))
df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)
top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "Nigerian-TCGAblack-TNBC.csv", row.names = FALSE)
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA White - HR+2-
designNvsWHR <- design
designNvsWHR$sampleCondition <- ifelse (designNvsWHR$sampleCondition=="TCGA_white.HRpos", 0, as.character(designNvsWHR$sampleCondition))
designNvsWHR$sampleCondition <- ifelse (designNvsWHR$sampleCondition=="Nigerian.HRpos", 1, as.character(designNvsWHR$sampleCondition))
designNvsWHR$sampleCondition <- ifelse (designNvsWHR$sampleCondition==0 | designNvsWHR$sampleCondition==1, designNvsWHR$sampleCondition, NA)
designNvsWHR <- designNvsWHR %>% subset(is.na(sampleCondition)==FALSE)
designNvsWHR$TCGA_white.HRpos <- ifelse (designNvsWHR$sampleCondition==0, 1, 0)
designNvsWHR$Nigerian.HRpos <- ifelse (designNvsWHR$sampleCondition==1, 1, 0)
designNvsWHR$sampleCondition <- NULL
mm <- model.matrix(~0+designNvsWHR$TCGA_white.HRpos+designNvsWHR$Nigerian.HRpos)
quantids <- rownames(designNvsWHR)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "Nigerian")
quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)
d0 <- DGEList(counts=quantdata, genes=annotation)
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # Number of genes after taking out low expressed genes
[1] 13250 17
v=voom(d,designNvsWHR,plot=T, normalize="quantile")
contr.matrix <- makeContrasts(TCGA_white.HRpos-Nigerian.HRpos, levels=colnames(designNvsWHR))
fit <- lmFit(v, designNvsWHR)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
TCGA_white.HRpos - Nigerian.HRpos
Down 579
NotSig 12031
Up 640
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between HR-positive breast cancers in Nigerian and TCGA white patients\n quantile corrected")
qvals<-p.adjust(fit$p.value[,1], method='fdr')
df_limma <- data_frame(log2FoldChange = fit$coefficients[,1],
pval = fit$p.value[,1],
padj = qvals,
anno = fit$genes)
with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between HR-postive \nbreast cancers in Nigerian and TCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))
df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)
top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGA_white-Nigerian-HRpos.csv", row.names = FALSE)
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA Black - HR+2-
designNvsBHR <- design
designNvsBHR$sampleCondition <- ifelse (designNvsBHR$sampleCondition=="TCGA_black.HRpos", 0, as.character(designNvsBHR$sampleCondition))
designNvsBHR$sampleCondition <- ifelse (designNvsBHR$sampleCondition=="Nigerian.HRpos", 1, as.character(designNvsBHR$sampleCondition))
designNvsBHR$sampleCondition <- ifelse (designNvsBHR$sampleCondition==0 | designNvsBHR$sampleCondition==1, designNvsBHR$sampleCondition, NA)
designNvsBHR <- designNvsBHR %>% subset(is.na(sampleCondition)==FALSE)
designNvsBHR$TCGA_black.HRpos <- ifelse (designNvsBHR$sampleCondition==0, 1, 0)
designNvsBHR$Nigerian.HRpos <- ifelse (designNvsBHR$sampleCondition==1, 1, 0)
designNvsBHR$sampleCondition <- NULL
mm <- model.matrix(~0+designNvsBHR$TCGA_black.HRpos+designNvsBHR$Nigerian.HRpos)
quantids <- rownames(designNvsBHR)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_black", "Nigerian")
quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)
d0 <- DGEList(counts=quantdata, genes=annotation)
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # Number of genes after taking out low expressed genes
[1] 15862 19
v=voom(d,designNvsBHR,plot=T, normalize="quantile")
contr.matrix <- makeContrasts(TCGA_black.HRpos-Nigerian.HRpos, levels=colnames(designNvsBHR))
fit <- lmFit(v, designNvsBHR)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
TCGA_black.HRpos - Nigerian.HRpos
Down 196
NotSig 15625
Up 41
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between HR-positive breast cancers in Nigerian and TCGA black patients\n quantile corrected")
qvals<-p.adjust(fit$p.value[,1], method='fdr')
df_limma <- data_frame(log2FoldChange = fit$coefficients[,1],
pval = fit$p.value[,1],
padj = qvals,
anno = fit$genes)
with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between HR-positive \nbreast cancers in Nigerian and TCGA black breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))
df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)
top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAblack-Nigerian-HRpos.csv", row.names = FALSE)
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA White - HER2 (no TCGA Black HER2+ patients)
designNvsWHER2 <- design
designNvsWHER2$sampleCondition <- ifelse (designNvsWHER2$sampleCondition=="TCGA_white.Her2", 0, as.character(designNvsWHER2$sampleCondition))
designNvsWHER2$sampleCondition <- ifelse (designNvsWHER2$sampleCondition=="Nigerian.Her2", 1, as.character(designNvsWHER2$sampleCondition))
designNvsWHER2$sampleCondition <- ifelse (designNvsWHER2$sampleCondition==0 | designNvsWHER2$sampleCondition==1, designNvsWHER2$sampleCondition, NA)
designNvsWHER2 <- designNvsWHER2 %>% subset(is.na(sampleCondition)==FALSE)
designNvsWHER2$TCGA_white.Her2 <- ifelse (designNvsWHER2$sampleCondition==0, 1, 0)
designNvsWHER2$Nigerian.Her2 <- ifelse (designNvsWHER2$sampleCondition==1, 1, 0)
designNvsWHER2$sampleCondition <- NULL
mm <- model.matrix(~0+designNvsWHER2$TCGA_white.Her2+designNvsWHER2$Nigerian.Her2)
quantids <- rownames(designNvsWHER2)
rownames(mm) <- quantids
colnames(mm) <- c("TCGA_white", "Nigerian")
quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)
d0 <- DGEList(counts=quantdata, genes=annotation)
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # Number of genes after taking out low expressed genes
[1] 14148 33
v=voom(d,designNvsWHER2,plot=T, normalize="quantile")
contr.matrix <- makeContrasts(TCGA_white.Her2-Nigerian.Her2, levels=colnames(designNvsWHER2))
fit <- lmFit(v, designNvsWHER2)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
TCGA_white.Her2 - Nigerian.Her2
Down 1147
NotSig 11167
Up 1834
hist(fit$p.value, ylim=c(0,3000), main="Histogram of unadjusted p-values of differential gene expression\n between Her2+ breast cancers in Nigerian and TCGA white patients\n quantile corrected")
qvals<-p.adjust(fit$p.value[,1], method='fdr')
df_limma <- data_frame(log2FoldChange = fit$coefficients[,1],
pval = fit$p.value[,1],
padj = qvals,
anno = fit$genes)
with(df_limma, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between Her2+ \nbreast cancers in Nigerian and TCGA white breast cancer patients\nquantile corrected", xlim=c(-50,50), ylim=c(0,70)))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(df_limma, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=anno$symbol, cex=.5))
df_limmaprint <- as.data.frame(df_limma)
df_limmaprint <- df_limmaprint %>% arrange(log2FoldChange) %>% dplyr::filter(padj < 0.05) %>% dplyr::filter(abs(log2FoldChange)>0.58)
top_n(df_limmaprint, 10, log2FoldChange)
top_n(df_limmaprint, -10, log2FoldChange)
write.csv(df_limmaprint, file = "TCGAwhite-Nigerian-Her2.csv", row.names = FALSE)
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
##DE: Nigerian/TCGA - HER2 14q LOH
design <- t_norm_countmatrix
designNvsTLOH <- design %>% dplyr::select(condition3)
designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3=="noLOH", 0, as.character(designNvsTLOH$condition3))
designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3=="LOH", 1, as.character(designNvsTLOH$condition3))
designNvsTLOH$condition3 <- ifelse (designNvsTLOH$condition3==0 | designNvsTLOH$condition3==1, designNvsTLOH$condition3, NA)
designNvsTLOH <- designNvsTLOH %>% subset(is.na(condition3)==FALSE)
designNvsTLOH$noLOH <- ifelse (designNvsTLOH$condition3==0, 1, 0)
designNvsTLOH$LOH <- ifelse (designNvsTLOH$condition3==1, 1, 0)
designNvsTLOH$condition3 <- NULL
mm <- model.matrix(~0+designNvsTLOH$noLOH+designNvsTLOH$LOH)
quantids <- rownames(designNvsTLOH)
rownames(mm) <- quantids
colnames(mm) <- c("noLOH", "LOH")
quantdata <- as.data.frame(t(counts(ddsHTSeqMF)))
quantdata <- quantdata[quantids,]
quantdata <- t(quantdata)
d0 <- DGEList(counts=quantdata, genes=annotation)
cutoff <- 10
drop <- which(apply(cpm(d0), 1, max) < cutoff)
d <- d0[-drop,]
dim(d) # Number of genes after taking out low expressed genes
[1] 13844 21
v=voom(d,designNvsTLOH,plot=T, normalize="quantile")
contr.matrix <- makeContrasts(noLOH-LOH, levels=colnames(designNvsTLOH))
fit <- lmFit(v, designNvsTLOH)
fit <- contrasts.fit(fit, contrasts=contr.matrix)
fit <- eBayes(fit)
dt <- decideTests(fit)
summary(dt)
noLOH - LOH
Down 0
NotSig 13844
Up 0
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
No significant differential expression was identified via quantile normalization/voom, which is expected as this method can be overly convservative for inter-group differential expression estimation. We have previously validated DESeq2 for inter-Nigerian comparison.
sampleTable2 <- data.frame(sampleName=gsub(".htseq.counts","",sampleFiles),
fileName=sampleFiles,
sampleCondition=sampleLOH)
sampleTable2 <- sampleTable2 %>% subset(is.na(sampleCondition)==FALSE)
ddsHTSeqMF2 <- DESeqDataSetFromHTSeqCount(
sampleTable=sampleTable2,
directory=FOLDER,
design=~sampleCondition)
ddsHTSeqMF2 <- ddsHTSeqMF2[rowSums(counts(ddsHTSeqMF2)) > 0, ] #Pre-filtering the dataset by removing the rows without any information about gene expression
dds <- estimateSizeFactors(ddsHTSeqMF2) #The size factor is the median ratio of the sample over a "pseudosample": for each gene, the geometric mean of all samples. This accounts for sequencing depth.
vsd <- vst(ddsHTSeqMF2, blind=FALSE) #Variance-stabilizing transformation, only using this since >50 samples
plotPCA(vsd, intgroup=c("sampleCondition"))
ddsMF <- DESeq(ddsHTSeqMF2)
resultsNames(ddsMF)
[1] "Intercept" "sampleCondition_noLOH_vs_LOH"
fc = 0.58 #Subsequent threshold of signifcance for log2 fold change -> 0.58 = log2(1.5)
fdr = 0.05 #Subsequent threshold of significance for p-value (adjusted by FDR)
cat("MA Plot: Differential expression based on 14q LOH presence")
MA Plot: Differential expression based on 14q LOH presence
res <- lfcShrink(ddsMF, coef="sampleCondition_noLOH_vs_LOH", type="ashr", optmethod = "mixEM")
DESeq2::plotMA(res, ylim=c(-10,10), xlim=c(0.1,200))
diffLOH<- results(ddsMF, contrast=c("sampleCondition", "noLOH", "LOH"), alpha=0.05)
nrow(diffLOH)
[1] 19071
diffLOH <- diffLOH[(diffLOH$log2FoldChange >= fc | diffLOH$log2FoldChange <= -fc),]
diffLOH <- subset(diffLOH, padj < fdr)
nrow(diffLOH)
[1] 101
restemp <- lfcShrink(ddsMF, contrast=c("sampleCondition", "noLOH", "LOH"), res = diffLOH, type="ashr", optmethod = "mixEM")
restemp$temp <- row.names(restemp)
restemp$temp <- gsub("[.].+", "", restemp$temp)
restemp$symbol <- mapIds(EnsDb.Hsapiens.v75,
keys=restemp$temp,
column="SYMBOL",
keytype="GENEID",
multiVals="first")
with(restemp, plot(log2FoldChange, -log10(padj), pch=20, main="Volcano plot of differential gene expression between \nbreast cancers in Nigerian patients with and without 14q LOH", xlim=c(-60,60), ylim=c(0,60)))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), points(log2FoldChange, -log10(padj), pch=20, col="blue"))
with(subset(restemp, padj<0.05 & (2^(abs(log2FoldChange))>50)), textxy(log2FoldChange, -log10(padj), labs=symbol, cex=.5))
diffLOHtable <- as.data.frame(diffLOH)
diffLOHtable$temp <- row.names(diffLOH)
diffLOHtable$temp <- gsub("[.].+", "", diffLOHtable$temp)
diffLOHtable$symbol <- mapIds(EnsDb.Hsapiens.v75,
keys=diffLOHtable$temp,
column="SYMBOL",
keytype="GENEID",
multiVals="first")
diffLOHtable$biotype <- mapIds(EnsDb.Hsapiens.v75,
keys=diffLOHtable$temp,
column="GENEBIOTYPE",
keytype="GENEID",
multiVals="first")
diffLOHtable$chr <- mapIds(EnsDb.Hsapiens.v75,
keys=diffLOHtable$temp,
column="SEQNAME",
keytype="GENEID",
multiVals="first")
diffLOHtable$locstart <- mapIds(EnsDb.Hsapiens.v75,
keys=diffLOHtable$temp,
column="GENESEQSTART",
keytype="GENEID",
multiVals="first")
diffLOHtable$locend <- mapIds(EnsDb.Hsapiens.v75,
keys=diffLOHtable$temp,
column="GENESEQEND",
keytype="GENEID",
multiVals="first")
diffLOHtable$temp <- NULL
diffLOHtable <- diffLOHtable %>% arrange(log2FoldChange)
top_n(diffLOHtable, 10, log2FoldChange)
top_n(diffLOHtable, -10, log2FoldChange)
write.csv(diffLOHtable, file = "DE-LOH.csv")
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] parallel stats4 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] Glimma_1.12.0 RColorBrewer_1.1-2
[3] preprocessCore_1.46.0 ashr_2.2-32
[5] ggfortify_0.4.7 calibrate_1.7.2
[7] MASS_7.3-51.4 sva_3.32.1
[9] mgcv_1.8-28 nlme_3.1-140
[11] EnsDb.Hsapiens.v75_2.99.0 ensembldb_2.8.0
[13] AnnotationFilter_1.8.0 GenomicFeatures_1.36.1
[15] hexbin_1.27.3 stringi_1.4.3
[17] dplyr_0.8.1 affy_1.62.0
[19] checkmate_1.9.3 pathview_1.24.0
[21] org.Hs.eg.db_3.8.2 AnnotationDbi_1.46.0
[23] clusterProfiler_3.12.0 pheatmap_1.0.12
[25] genefilter_1.66.0 vsn_3.52.0
[27] RUVSeq_1.18.0 EDASeq_2.18.0
[29] ShortRead_1.42.0 GenomicAlignments_1.20.0
[31] Rsamtools_2.0.0 Biostrings_2.52.0
[33] XVector_0.24.0 DESeq2_1.24.0
[35] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[37] BiocParallel_1.18.0 matrixStats_0.54.0
[39] Biobase_2.44.0 GenomicRanges_1.36.0
[41] GenomeInfoDb_1.20.0 IRanges_2.18.1
[43] S4Vectors_0.22.0 BiocGenerics_0.30.0
[45] edgeR_3.26.4 limma_3.40.2
[47] ggbiplot_0.55 scales_1.0.0
[49] plyr_1.8.4 ggplot2_3.1.1
[51] gplots_3.0.1.1
loaded via a namespace (and not attached):
[1] R.utils_2.8.0 tidyselect_0.2.5 RSQLite_2.1.1
[4] htmlwidgets_1.3 DESeq_1.36.0 munsell_0.5.0
[7] codetools_0.2-16 withr_2.1.2 colorspace_1.4-1
[10] GOSemSim_2.10.0 knitr_1.23 rstudioapi_0.10
[13] pscl_1.5.2 DOSE_3.10.1 labeling_0.3
[16] git2r_0.25.2 KEGGgraph_1.44.0 urltools_1.7.3
[19] GenomeInfoDbData_1.2.1 mixsqp_0.1-97 hwriter_1.3.2
[22] polyclip_1.10-0 bit64_0.9-7 farver_1.1.0
[25] rprojroot_1.3-2 xfun_0.7 doParallel_1.0.14
[28] R6_2.4.0 locfit_1.5-9.1 bitops_1.0-6
[31] fgsea_1.10.0 gridGraphics_0.4-1 assertthat_0.2.1
[34] ggraph_1.0.2 nnet_7.3-12 enrichplot_1.4.0
[37] gtable_0.3.0 workflowr_1.4.0 rlang_0.3.4
[40] splines_3.6.0 rtracklayer_1.44.0 lazyeval_0.2.2
[43] acepack_1.4.1 europepmc_0.3 BiocManager_1.30.4
[46] yaml_2.2.0 reshape2_1.4.3 backports_1.1.4
[49] qvalue_2.16.0 Hmisc_4.2-0 tools_3.6.0
[52] ggplotify_0.0.3 affyio_1.54.0 ggridges_0.5.1
[55] Rcpp_1.0.1 base64enc_0.1-3 progress_1.2.2
[58] zlibbioc_1.30.0 purrr_0.3.2 RCurl_1.95-4.12
[61] prettyunits_1.0.2 rpart_4.1-15 viridis_0.5.1
[64] cowplot_0.9.4 ggrepel_0.8.1 cluster_2.0.9
[67] fs_1.3.1 magrittr_1.5 data.table_1.12.2
[70] DO.db_2.9 triebeard_0.3.0 truncnorm_1.0-8
[73] SQUAREM_2017.10-1 ProtGenerics_1.16.0 aroma.light_3.14.0
[76] hms_0.4.2 evaluate_0.14 xtable_1.8-4
[79] XML_3.98-1.20 gridExtra_2.3 compiler_3.6.0
[82] biomaRt_2.40.0 tibble_2.1.3 KernSmooth_2.23-15
[85] crayon_1.3.4 R.oo_1.22.0 htmltools_0.3.6
[88] Formula_1.2-3 tidyr_0.8.3 geneplotter_1.62.0
[91] DBI_1.0.0 tweenr_1.0.1 Matrix_1.2-17
[94] R.methodsS3_1.7.1 gdata_2.18.0 igraph_1.2.4.1
[97] pkgconfig_2.0.2 rvcheck_0.1.3 foreign_0.8-71
[100] foreach_1.4.4 xml2_1.2.0 annotate_1.62.0
[103] stringr_1.4.0 digest_0.6.19 graph_1.62.0
[106] rmarkdown_1.13 fastmatch_1.1-0 htmlTable_1.13.1
[109] curl_3.3 gtools_3.8.1 jsonlite_1.6
[112] viridisLite_0.3.0 pillar_1.4.1 lattice_0.20-38
[115] KEGGREST_1.24.0 httr_1.4.0 survival_2.44-1.1
[118] GO.db_3.8.2 glue_1.3.1 UpSetR_1.4.0
[121] iterators_1.0.10 png_0.1-7 bit_1.1-14
[124] Rgraphviz_2.28.0 ggforce_0.2.2 blob_1.1.1
[127] latticeExtra_0.6-28 caTools_1.17.1.2 memoise_1.1.0